Overview

Dataset statistics

Number of variables21
Number of observations12997
Missing cells4609
Missing cells (%)1.7%
Duplicate rows1126
Duplicate rows (%)8.7%
Total size in memory2.1 MiB
Average record size in memory168.0 B

Variable types

Categorical8
Numeric11
Boolean2

Alerts

Dataset has 1126 (8.7%) duplicate rowsDuplicates
0 has a high cardinality: 3193 distinct valuesHigh cardinality
3 is highly overall correlated with 11 and 1 other fieldsHigh correlation
4 is highly overall correlated with 15 and 1 other fieldsHigh correlation
6 is highly overall correlated with 9 and 1 other fieldsHigh correlation
9 is highly overall correlated with 6 and 1 other fieldsHigh correlation
10 is highly overall correlated with 6 and 1 other fieldsHigh correlation
11 is highly overall correlated with 3 and 1 other fieldsHigh correlation
12 is highly overall correlated with 11 and 3 other fieldsHigh correlation
15 is highly overall correlated with 4 and 2 other fieldsHigh correlation
16 is highly overall correlated with 4 and 2 other fieldsHigh correlation
20 is highly overall correlated with 3High correlation
18 is highly overall correlated with 12High correlation
7 has 300 (2.3%) missing valuesMissing
15 has 304 (2.3%) missing valuesMissing
16 has 382 (2.9%) missing valuesMissing
17 has 3198 (24.6%) missing valuesMissing
0 is uniformly distributedUniform
2 has 8640 (66.5%) zerosZeros
4 has 383 (2.9%) zerosZeros
9 has 295 (2.3%) zerosZeros
15 has 1123 (8.6%) zerosZeros
16 has 857 (6.6%) zerosZeros

Reproduction

Analysis started2023-05-10 07:01:44.264953
Analysis finished2023-05-10 07:02:24.455377
Duration40.19 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

0
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3193
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size101.7 KiB
2011-01-24
 
8
2011-09-16
 
8
2010-01-25
 
7
2010-02-20
 
7
2012-07-19
 
7
Other values (3188)
12960 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters129970
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2009-03-08
2nd row2014-11-12
3rd row2008-08-08
4th row2015-10-12
5th row2013-10-27

Common Values

ValueCountFrequency (%)
2011-01-24 8
 
0.1%
2011-09-16 8
 
0.1%
2010-01-25 7
 
0.1%
2010-02-20 7
 
0.1%
2012-07-19 7
 
0.1%
2016-10-05 7
 
0.1%
2010-03-29 7
 
0.1%
2012-05-17 7
 
0.1%
2016-09-03 7
 
0.1%
2017-01-22 7
 
0.1%
Other values (3183) 12925
99.4%

Length

2023-05-10T07:02:24.685802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-01-24 8
 
0.1%
2011-09-16 8
 
0.1%
2010-03-19 7
 
0.1%
2017-06-07 7
 
0.1%
2016-11-15 7
 
0.1%
2013-05-16 7
 
0.1%
2013-09-30 7
 
0.1%
2012-08-02 7
 
0.1%
2009-08-24 7
 
0.1%
2011-11-08 7
 
0.1%
Other values (3183) 12925
99.4%

Most occurring characters

ValueCountFrequency (%)
0 32966
25.4%
- 25994
20.0%
1 23286
17.9%
2 21786
16.8%
3 4412
 
3.4%
9 4004
 
3.1%
6 3810
 
2.9%
4 3692
 
2.8%
5 3609
 
2.8%
8 3216
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103976
80.0%
Dash Punctuation 25994
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32966
31.7%
1 23286
22.4%
2 21786
21.0%
3 4412
 
4.2%
9 4004
 
3.9%
6 3810
 
3.7%
4 3692
 
3.6%
5 3609
 
3.5%
8 3216
 
3.1%
7 3195
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 25994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 129970
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32966
25.4%
- 25994
20.0%
1 23286
17.9%
2 21786
16.8%
3 4412
 
3.4%
9 4004
 
3.1%
6 3810
 
2.9%
4 3692
 
2.8%
5 3609
 
2.8%
8 3216
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32966
25.4%
- 25994
20.0%
1 23286
17.9%
2 21786
16.8%
3 4412
 
3.4%
9 4004
 
3.1%
6 3810
 
2.9%
4 3692
 
2.8%
5 3609
 
2.8%
8 3216
 
2.5%

1
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.7 KiB
Woodlands
3515 
Tuas
3497 
Changi
3457 
Sentosa
2528 

Length

Max length9
Median length7
Mean length6.4677233
Min length4

Characters and Unicode

Total characters84061
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChangi
2nd rowWoodlands
3rd rowWoodlands
4th rowChangi
5th rowWoodlands

Common Values

ValueCountFrequency (%)
Woodlands 3515
27.0%
Tuas 3497
26.9%
Changi 3457
26.6%
Sentosa 2528
19.5%

Length

2023-05-10T07:02:25.081716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T07:02:25.564177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
woodlands 3515
27.0%
tuas 3497
26.9%
changi 3457
26.6%
sentosa 2528
19.5%

Most occurring characters

ValueCountFrequency (%)
a 12997
15.5%
o 9558
11.4%
s 9540
11.3%
n 9500
11.3%
d 7030
 
8.4%
W 3515
 
4.2%
l 3515
 
4.2%
T 3497
 
4.2%
u 3497
 
4.2%
C 3457
 
4.1%
Other values (6) 17955
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71064
84.5%
Uppercase Letter 12997
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12997
18.3%
o 9558
13.4%
s 9540
13.4%
n 9500
13.4%
d 7030
9.9%
l 3515
 
4.9%
u 3497
 
4.9%
h 3457
 
4.9%
g 3457
 
4.9%
i 3457
 
4.9%
Other values (2) 5056
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
W 3515
27.0%
T 3497
26.9%
C 3457
26.6%
S 2528
19.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 84061
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12997
15.5%
o 9558
11.4%
s 9540
11.3%
n 9500
11.3%
d 7030
 
8.4%
W 3515
 
4.2%
l 3515
 
4.2%
T 3497
 
4.2%
u 3497
 
4.2%
C 3457
 
4.1%
Other values (6) 17955
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84061
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12997
15.5%
o 9558
11.4%
s 9540
11.3%
n 9500
11.3%
d 7030
 
8.4%
W 3515
 
4.2%
l 3515
 
4.2%
T 3497
 
4.2%
u 3497
 
4.2%
C 3457
 
4.1%
Other values (6) 17955
21.4%

2
Real number (ℝ)

Distinct334
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0405324
Minimum0
Maximum367.6
Zeros8640
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:25.887358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile16.8
Maximum367.6
Range367.6
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation10.958255
Coefficient of variation (CV)3.604058
Kurtosis165.10198
Mean3.0405324
Median Absolute Deviation (MAD)0
Skewness9.5486922
Sum39517.8
Variance120.08336
MonotonicityNot monotonic
2023-05-10T07:02:26.422439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8640
66.5%
0.2 553
 
4.3%
0.4 305
 
2.3%
0.6 201
 
1.5%
0.8 140
 
1.1%
1.2 127
 
1.0%
1 109
 
0.8%
1.6 101
 
0.8%
1.8 94
 
0.7%
1.4 91
 
0.7%
Other values (324) 2636
 
20.3%
ValueCountFrequency (%)
0 8640
66.5%
0.2 553
 
4.3%
0.4 305
 
2.3%
0.6 201
 
1.5%
0.8 140
 
1.1%
1 109
 
0.8%
1.2 127
 
1.0%
1.4 91
 
0.7%
1.6 101
 
0.8%
1.8 94
 
0.7%
ValueCountFrequency (%)
367.6 1
< 0.1%
210.6 1
< 0.1%
184.6 1
< 0.1%
182.6 1
< 0.1%
182.2 1
< 0.1%
168.4 1
< 0.1%
162.2 1
< 0.1%
145.6 1
< 0.1%
145.2 1
< 0.1%
145 1
< 0.1%

3
Real number (ℝ)

Distinct114
Distinct (%)0.9%
Missing80
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5.5519548
Minimum0
Maximum44
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:26.932214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q13.4
median5.4
Q37.4
95-th percentile10.4
Maximum44
Range44
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8987084
Coefficient of variation (CV)0.52210591
Kurtosis2.9201786
Mean5.5519548
Median Absolute Deviation (MAD)2
Skewness0.72963294
Sum71714.6
Variance8.4025103
MonotonicityNot monotonic
2023-05-10T07:02:27.474424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 502
 
3.9%
8 462
 
3.6%
7 355
 
2.7%
6.4 346
 
2.7%
5.6 343
 
2.6%
6.8 330
 
2.5%
5 318
 
2.4%
7.6 318
 
2.4%
7.4 314
 
2.4%
5.8 314
 
2.4%
Other values (104) 9315
71.7%
ValueCountFrequency (%)
0 22
 
0.2%
0.2 41
 
0.3%
0.4 54
 
0.4%
0.6 84
 
0.6%
0.7 2
 
< 0.1%
0.8 111
0.9%
1 141
1.1%
1.2 170
1.3%
1.4 178
1.4%
1.6 236
1.8%
ValueCountFrequency (%)
44 1
< 0.1%
23.8 1
< 0.1%
22.8 1
< 0.1%
21.6 1
< 0.1%
20.4 1
< 0.1%
20.2 1
< 0.1%
19.8 1
< 0.1%
19.4 1
< 0.1%
19.2 1
< 0.1%
19 1
< 0.1%

4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct275
Distinct (%)2.1%
Missing58
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.4962362
Minimum-13.5
Maximum13.9
Zeros383
Zeros (%)2.9%
Negative1230
Negative (%)9.5%
Memory size101.7 KiB
2023-05-10T07:02:27.755268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-13.5
5-th percentile-9.2
Q13.95
median8.7
Q310.7
95-th percentile12.6
Maximum13.9
Range27.4
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation6.0262358
Coefficient of variation (CV)0.92765035
Kurtosis1.8003173
Mean6.4962362
Median Absolute Deviation (MAD)2.5
Skewness-1.5155187
Sum84054.8
Variance36.315518
MonotonicityNot monotonic
2023-05-10T07:02:28.024957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 383
 
2.9%
10.8 271
 
2.1%
11 268
 
2.1%
11.1 265
 
2.0%
10.9 252
 
1.9%
11.2 220
 
1.7%
10.5 219
 
1.7%
10.7 208
 
1.6%
10.6 208
 
1.6%
10.2 204
 
1.6%
Other values (265) 10441
80.3%
ValueCountFrequency (%)
-13.5 1
 
< 0.1%
-13.4 2
 
< 0.1%
-13.3 5
 
< 0.1%
-13.2 19
0.1%
-13.1 14
0.1%
-13 21
0.2%
-12.9 7
 
0.1%
-12.8 7
 
0.1%
-12.7 5
 
< 0.1%
-12.6 5
 
< 0.1%
ValueCountFrequency (%)
13.9 2
 
< 0.1%
13.8 3
 
< 0.1%
13.7 9
 
0.1%
13.6 4
 
< 0.1%
13.5 10
 
0.1%
13.4 24
 
0.2%
13.3 58
0.4%
13.2 107
0.8%
13.1 108
0.8%
13 83
0.6%

5
Categorical

Distinct18
Distinct (%)0.1%
Missing84
Missing (%)0.6%
Memory size101.7 KiB
E
1333 
N
1210 
SW
1107 
ENE
906 
ESE
881 
Other values (13)
7476 

Length

Max length3
Median length2
Mean length2.1470611
Min length1

Characters and Unicode

Total characters27725
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowWNW
3rd rowESE
4th rowNE
5th rowNNW

Common Values

ValueCountFrequency (%)
E 1333
 
10.3%
N 1210
 
9.3%
SW 1107
 
8.5%
ENE 906
 
7.0%
ESE 881
 
6.8%
NE 841
 
6.5%
SSW 832
 
6.4%
W 784
 
6.0%
WNW 752
 
5.8%
SE 713
 
5.5%
Other values (8) 3554
27.3%

Length

2023-05-10T07:02:28.315886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e 1333
 
10.3%
n 1210
 
9.4%
sw 1107
 
8.6%
ene 906
 
7.0%
ese 881
 
6.8%
ne 841
 
6.5%
ssw 832
 
6.4%
w 784
 
6.1%
wnw 752
 
5.8%
se 713
 
5.5%
Other values (8) 3554
27.5%

Most occurring characters

ValueCountFrequency (%)
E 7701
27.8%
S 7007
25.3%
W 6724
24.3%
N 6293
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 27725
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 7701
27.8%
S 7007
25.3%
W 6724
24.3%
N 6293
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 27725
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 7701
27.8%
S 7007
25.3%
W 6724
24.3%
N 6293
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 7701
27.8%
S 7007
25.3%
W 6724
24.3%
N 6293
22.7%

6
Real number (ℝ)

Distinct53
Distinct (%)0.4%
Missing80
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean36.640164
Minimum9
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:28.581442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20
Q128
median35
Q343
95-th percentile59
Maximum126
Range117
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.628845
Coefficient of variation (CV)0.34467217
Kurtosis2.5111468
Mean36.640164
Median Absolute Deviation (MAD)7
Skewness1.1799829
Sum473281
Variance159.48773
MonotonicityNot monotonic
2023-05-10T07:02:28.875869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 988
 
7.6%
31 968
 
7.4%
33 929
 
7.1%
37 864
 
6.6%
39 831
 
6.4%
30 808
 
6.2%
28 749
 
5.8%
41 683
 
5.3%
26 612
 
4.7%
24 590
 
4.5%
Other values (43) 4895
37.7%
ValueCountFrequency (%)
9 2
 
< 0.1%
11 6
 
< 0.1%
13 40
 
0.3%
15 111
 
0.9%
17 168
 
1.3%
19 290
2.2%
20 407
3.1%
22 420
3.2%
24 590
4.5%
26 612
4.7%
ValueCountFrequency (%)
126 1
 
< 0.1%
104 2
 
< 0.1%
102 4
< 0.1%
100 3
 
< 0.1%
98 3
 
< 0.1%
96 9
0.1%
94 5
< 0.1%
93 9
0.1%
91 6
< 0.1%
89 9
0.1%

7
Categorical

Distinct16
Distinct (%)0.1%
Missing300
Missing (%)2.3%
Memory size101.7 KiB
N
1212 
E
1209 
SW
1159 
SE
1043 
WSW
849 
Other values (11)
7225 

Length

Max length3
Median length2
Mean length2.1221548
Min length1

Characters and Unicode

Total characters26945
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowNNE
3rd rowSE
4th rowNNW
5th rowN

Common Values

ValueCountFrequency (%)
N 1212
 
9.3%
E 1209
 
9.3%
SW 1159
 
8.9%
SE 1043
 
8.0%
WSW 849
 
6.5%
ENE 843
 
6.5%
ESE 839
 
6.5%
NE 827
 
6.4%
W 796
 
6.1%
SSE 766
 
5.9%
Other values (6) 3154
24.3%

Length

2023-05-10T07:02:29.164620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 1212
 
9.5%
e 1209
 
9.5%
sw 1159
 
9.1%
se 1043
 
8.2%
wsw 849
 
6.7%
ene 843
 
6.6%
ese 839
 
6.6%
ne 827
 
6.5%
w 796
 
6.3%
sse 766
 
6.0%
Other values (6) 3154
24.8%

Most occurring characters

ValueCountFrequency (%)
E 7844
29.1%
S 7551
28.0%
W 5932
22.0%
N 5618
20.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26945
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 7844
29.1%
S 7551
28.0%
W 5932
22.0%
N 5618
20.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 26945
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 7844
29.1%
S 7551
28.0%
W 5932
22.0%
N 5618
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 7844
29.1%
S 7551
28.0%
W 5932
22.0%
N 5618
20.8%

8
Categorical

Distinct16
Distinct (%)0.1%
Missing52
Missing (%)0.4%
Memory size101.7 KiB
SW
997 
WNW
983 
ENE
968 
E
928 
N
903 
Other values (11)
8166 

Length

Max length3
Median length2
Mean length2.2187717
Min length1

Characters and Unicode

Total characters28722
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowESE
2nd rowNW
3rd rowE
4th rowNE
5th rowN

Common Values

ValueCountFrequency (%)
SW 997
 
7.7%
WNW 983
 
7.6%
ENE 968
 
7.4%
E 928
 
7.1%
N 903
 
6.9%
W 888
 
6.8%
NE 864
 
6.6%
SSW 850
 
6.5%
NNW 809
 
6.2%
ESE 755
 
5.8%
Other values (6) 4000
30.8%

Length

2023-05-10T07:02:29.454233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sw 997
 
7.7%
wnw 983
 
7.6%
ene 968
 
7.5%
e 928
 
7.2%
n 903
 
7.0%
w 888
 
6.9%
ne 864
 
6.7%
ssw 850
 
6.6%
nnw 809
 
6.2%
ese 755
 
5.8%
Other values (6) 4000
30.9%

Most occurring characters

ValueCountFrequency (%)
W 7752
27.0%
N 7290
25.4%
E 7022
24.4%
S 6658
23.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 28722
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 7752
27.0%
N 7290
25.4%
E 7022
24.4%
S 6658
23.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 28722
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 7752
27.0%
N 7290
25.4%
E 7022
24.4%
S 6658
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28722
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 7752
27.0%
N 7290
25.4%
E 7022
24.4%
S 6658
23.2%

9
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.456974
Minimum0
Maximum65
Zeros295
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:29.712129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median11
Q317
95-th percentile28
Maximum65
Range65
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3418141
Coefficient of variation (CV)0.66965015
Kurtosis3.9607512
Mean12.456974
Median Absolute Deviation (MAD)4
Skewness1.5972726
Sum161841
Variance69.585863
MonotonicityNot monotonic
2023-05-10T07:02:29.969641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
9 1633
12.6%
7 1525
11.7%
6 1359
10.5%
11 1330
10.2%
13 1214
9.3%
15 953
7.3%
4 885
 
6.8%
17 785
 
6.0%
19 526
 
4.0%
20 472
 
3.6%
Other values (26) 2310
17.8%
ValueCountFrequency (%)
0 295
 
2.3%
2 434
 
3.3%
4 885
6.8%
6 1359
10.5%
7 1525
11.7%
9 1633
12.6%
11 1330
10.2%
13 1214
9.3%
15 953
7.3%
17 785
6.0%
ValueCountFrequency (%)
65 1
 
< 0.1%
63 2
 
< 0.1%
61 2
 
< 0.1%
59 1
 
< 0.1%
57 4
 
< 0.1%
56 8
 
0.1%
54 9
0.1%
52 15
0.1%
50 17
0.1%
48 20
0.2%

10
Real number (ℝ)

Distinct36
Distinct (%)0.3%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.703102
Minimum0
Maximum76
Zeros46
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:30.256836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median15
Q320
95-th percentile31
Maximum76
Range76
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.8825084
Coefficient of variation (CV)0.47191883
Kurtosis1.5174679
Mean16.703102
Median Absolute Deviation (MAD)5
Skewness0.85509063
Sum216990
Variance62.133939
MonotonicityNot monotonic
2023-05-10T07:02:30.523204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
13 1402
10.8%
15 1294
10.0%
11 1283
9.9%
17 1257
9.7%
9 1119
8.6%
19 973
 
7.5%
20 962
 
7.4%
24 714
 
5.5%
22 699
 
5.4%
7 650
 
5.0%
Other values (26) 2638
20.3%
ValueCountFrequency (%)
0 46
 
0.4%
2 120
 
0.9%
4 237
 
1.8%
6 448
 
3.4%
7 650
5.0%
9 1119
8.6%
11 1283
9.9%
13 1402
10.8%
15 1294
10.0%
17 1257
9.7%
ValueCountFrequency (%)
76 1
 
< 0.1%
65 1
 
< 0.1%
61 2
 
< 0.1%
59 1
 
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
54 1
 
< 0.1%
52 6
 
< 0.1%
50 16
0.1%
48 9
0.1%

11
Real number (ℝ)

Distinct89
Distinct (%)0.7%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.476623
Minimum11
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:30.804260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile39
Q156
median66
Q376
95-th percentile91
Maximum100
Range89
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.460822
Coefficient of variation (CV)0.23612736
Kurtosis0.021124412
Mean65.476623
Median Absolute Deviation (MAD)10
Skewness-0.29493617
Sum850083
Variance239.03702
MonotonicityNot monotonic
2023-05-10T07:02:31.096345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 357
 
2.7%
64 356
 
2.7%
69 351
 
2.7%
65 340
 
2.6%
58 339
 
2.6%
66 333
 
2.6%
72 333
 
2.6%
62 332
 
2.6%
70 329
 
2.5%
67 325
 
2.5%
Other values (79) 9588
73.8%
ValueCountFrequency (%)
11 1
 
< 0.1%
13 6
< 0.1%
14 5
< 0.1%
15 6
< 0.1%
16 6
< 0.1%
17 10
0.1%
18 11
0.1%
19 6
< 0.1%
20 8
0.1%
21 11
0.1%
ValueCountFrequency (%)
100 11
 
0.1%
99 32
 
0.2%
98 51
0.4%
97 44
0.3%
96 85
0.7%
95 68
0.5%
94 86
0.7%
93 109
0.8%
92 85
0.7%
91 96
0.7%

12
Real number (ℝ)

Distinct96
Distinct (%)0.7%
Missing27
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean50.818813
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:31.722972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile22
Q140
median51
Q361
95-th percentile81
Maximum100
Range95
Interquartile range (IQR)21

Descriptive statistics

Standard deviation16.865362
Coefficient of variation (CV)0.33187242
Kurtosis0.042570941
Mean50.818813
Median Absolute Deviation (MAD)10
Skewness0.11666665
Sum659120
Variance284.44045
MonotonicityNot monotonic
2023-05-10T07:02:32.014684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 381
 
2.9%
57 375
 
2.9%
52 358
 
2.8%
49 349
 
2.7%
51 346
 
2.7%
50 345
 
2.7%
54 337
 
2.6%
45 335
 
2.6%
56 329
 
2.5%
48 327
 
2.5%
Other values (86) 9488
73.0%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 11
 
0.1%
9 11
 
0.1%
10 17
0.1%
11 25
0.2%
12 32
0.2%
13 30
0.2%
14 30
0.2%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 2
 
< 0.1%
98 10
 
0.1%
97 11
 
0.1%
96 27
0.2%
95 23
0.2%
94 27
0.2%
93 31
0.2%
92 28
0.2%
91 46
0.4%

13
Categorical

Distinct32
Distinct (%)0.2%
Missing2
Missing (%)< 0.1%
Memory size101.7 KiB
meD
1030 
Med
1000 
mEd
983 
MEd
979 
MeD
978 
Other values (27)
8025 

Length

Max length4
Median length3
Mean length3.2066179
Min length3

Characters and Unicode

Total characters41670
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMED
2nd rowMeD
3rd rowMed
4th rowhIgh
5th rowmeD

Common Values

ValueCountFrequency (%)
meD 1030
 
7.9%
Med 1000
 
7.7%
mEd 983
 
7.6%
MEd 979
 
7.5%
MeD 978
 
7.5%
med 960
 
7.4%
mED 947
 
7.3%
MED 918
 
7.1%
lOw 353
 
2.7%
lOW 326
 
2.5%
Other values (22) 4521
34.8%

Length

2023-05-10T07:02:32.303662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
med 7795
60.0%
high 2685
 
20.7%
low 2515
 
19.4%

Most occurring characters

ValueCountFrequency (%)
e 3968
 
9.5%
d 3922
 
9.4%
m 3920
 
9.4%
M 3875
 
9.3%
D 3873
 
9.3%
E 3827
 
9.2%
h 2705
 
6.5%
H 2665
 
6.4%
g 1369
 
3.3%
i 1346
 
3.2%
Other values (8) 10200
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21029
50.5%
Uppercase Letter 20641
49.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3968
18.9%
d 3922
18.7%
m 3920
18.6%
h 2705
12.9%
g 1369
 
6.5%
i 1346
 
6.4%
w 1287
 
6.1%
l 1283
 
6.1%
o 1229
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 3875
18.8%
D 3873
18.8%
E 3827
18.5%
H 2665
12.9%
I 1339
 
6.5%
G 1316
 
6.4%
O 1286
 
6.2%
L 1232
 
6.0%
W 1228
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 41670
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3968
 
9.5%
d 3922
 
9.4%
m 3920
 
9.4%
M 3875
 
9.3%
D 3873
 
9.3%
E 3827
 
9.2%
h 2705
 
6.5%
H 2665
 
6.4%
g 1369
 
3.3%
i 1346
 
3.2%
Other values (8) 10200
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3968
 
9.5%
d 3922
 
9.4%
m 3920
 
9.4%
M 3875
 
9.3%
D 3873
 
9.3%
E 3827
 
9.2%
h 2705
 
6.5%
H 2665
 
6.4%
g 1369
 
3.3%
i 1346
 
3.2%
Other values (8) 10200
24.5%

14
Categorical

Distinct32
Distinct (%)0.2%
Missing8
Missing (%)0.1%
Memory size101.7 KiB
mEd
988 
meD
984 
mED
969 
med
964 
MEd
958 
Other values (27)
8126 

Length

Max length4
Median length3
Mean length3.208946
Min length3

Characters and Unicode

Total characters41681
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMed
2nd rowLOw
3rd rowMEd
4th rowmed
5th rowMeD

Common Values

ValueCountFrequency (%)
mEd 988
 
7.6%
meD 984
 
7.6%
mED 969
 
7.5%
med 964
 
7.4%
MEd 958
 
7.4%
MeD 953
 
7.3%
Med 930
 
7.2%
MED 902
 
6.9%
Low 352
 
2.7%
LoW 346
 
2.7%
Other values (22) 4643
35.7%

Length

2023-05-10T07:02:32.536103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
med 7648
58.9%
high 2714
 
20.9%
low 2627
 
20.2%

Most occurring characters

ValueCountFrequency (%)
m 3905
 
9.4%
d 3840
 
9.2%
e 3831
 
9.2%
E 3817
 
9.2%
D 3808
 
9.1%
M 3743
 
9.0%
h 2775
 
6.7%
H 2653
 
6.4%
i 1401
 
3.4%
g 1374
 
3.3%
Other values (8) 10534
25.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21081
50.6%
Uppercase Letter 20600
49.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 3905
18.5%
d 3840
18.2%
e 3831
18.2%
h 2775
13.2%
i 1401
 
6.6%
g 1374
 
6.5%
o 1340
 
6.4%
w 1327
 
6.3%
l 1288
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
E 3817
18.5%
D 3808
18.5%
M 3743
18.2%
H 2653
12.9%
G 1340
 
6.5%
L 1339
 
6.5%
I 1313
 
6.4%
W 1300
 
6.3%
O 1287
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 41681
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 3905
 
9.4%
d 3840
 
9.2%
e 3831
 
9.2%
E 3817
 
9.2%
D 3808
 
9.1%
M 3743
 
9.0%
h 2775
 
6.7%
H 2653
 
6.4%
i 1401
 
3.4%
g 1374
 
3.3%
Other values (8) 10534
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 3905
 
9.4%
d 3840
 
9.2%
e 3831
 
9.2%
E 3817
 
9.2%
D 3808
 
9.1%
M 3743
 
9.0%
h 2775
 
6.7%
H 2653
 
6.4%
i 1401
 
3.4%
g 1374
 
3.3%
Other values (8) 10534
25.3%

15
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing304
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean4.1088789
Minimum0
Maximum8
Zeros1123
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:32.750064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.7278248
Coefficient of variation (CV)0.66388542
Kurtosis-1.5609581
Mean4.1088789
Median Absolute Deviation (MAD)3
Skewness-0.11273596
Sum52154
Variance7.4410282
MonotonicityNot monotonic
2023-05-10T07:02:32.966481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 3301
25.4%
1 2548
19.6%
6 1288
 
9.9%
0 1123
 
8.6%
3 1036
 
8.0%
2 1016
 
7.8%
5 899
 
6.9%
8 802
 
6.2%
4 680
 
5.2%
(Missing) 304
 
2.3%
ValueCountFrequency (%)
0 1123
 
8.6%
1 2548
19.6%
2 1016
 
7.8%
3 1036
 
8.0%
4 680
 
5.2%
5 899
 
6.9%
6 1288
 
9.9%
7 3301
25.4%
8 802
 
6.2%
ValueCountFrequency (%)
8 802
 
6.2%
7 3301
25.4%
6 1288
 
9.9%
5 899
 
6.9%
4 680
 
5.2%
3 1036
 
8.0%
2 1016
 
7.8%
1 2548
19.6%
0 1123
 
8.6%

16
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)0.1%
Missing382
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean4.1241379
Minimum0
Maximum8
Zeros857
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:33.190773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.6461947
Coefficient of variation (CV)0.64163585
Kurtosis-1.5243568
Mean4.1241379
Median Absolute Deviation (MAD)3
Skewness-0.087150137
Sum52026
Variance7.0023466
MonotonicityNot monotonic
2023-05-10T07:02:33.414144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 3148
24.2%
1 2457
18.9%
6 1263
9.7%
2 1246
 
9.6%
3 1149
 
8.8%
5 988
 
7.6%
0 857
 
6.6%
8 762
 
5.9%
4 745
 
5.7%
(Missing) 382
 
2.9%
ValueCountFrequency (%)
0 857
 
6.6%
1 2457
18.9%
2 1246
 
9.6%
3 1149
 
8.8%
4 745
 
5.7%
5 988
 
7.6%
6 1263
9.7%
7 3148
24.2%
8 762
 
5.9%
ValueCountFrequency (%)
8 762
 
5.9%
7 3148
24.2%
6 1263
9.7%
5 988
 
7.6%
4 745
 
5.7%
3 1149
 
8.8%
2 1246
 
9.6%
1 2457
18.9%
0 857
 
6.6%

17
Boolean

Distinct2
Distinct (%)< 0.1%
Missing3198
Missing (%)24.6%
Memory size25.5 KiB
False
7488 
True
2311 
(Missing)
3198 
ValueCountFrequency (%)
False 7488
57.6%
True 2311
 
17.8%
(Missing) 3198
24.6%
2023-05-10T07:02:33.661022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

18
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
False
9998 
True
2999 
ValueCountFrequency (%)
False 9998
76.9%
True 2999
 
23.1%
2023-05-10T07:02:33.861169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

19
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.7 KiB
blue
3333 
white
3300 
red
3240 
grey
3124 

Length

Max length5
Median length4
Mean length4.0046164
Min length3

Characters and Unicode

Total characters52048
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue
2nd rowblue
3rd rowblue
4th rowred
5th rowgrey

Common Values

ValueCountFrequency (%)
blue 3333
25.6%
white 3300
25.4%
red 3240
24.9%
grey 3124
24.0%

Length

2023-05-10T07:02:34.072611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T07:02:34.341089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
blue 3333
25.6%
white 3300
25.4%
red 3240
24.9%
grey 3124
24.0%

Most occurring characters

ValueCountFrequency (%)
e 12997
25.0%
r 6364
12.2%
b 3333
 
6.4%
l 3333
 
6.4%
u 3333
 
6.4%
w 3300
 
6.3%
h 3300
 
6.3%
i 3300
 
6.3%
t 3300
 
6.3%
d 3240
 
6.2%
Other values (2) 6248
12.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52048
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12997
25.0%
r 6364
12.2%
b 3333
 
6.4%
l 3333
 
6.4%
u 3333
 
6.4%
w 3300
 
6.3%
h 3300
 
6.3%
i 3300
 
6.3%
t 3300
 
6.3%
d 3240
 
6.2%
Other values (2) 6248
12.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52048
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12997
25.0%
r 6364
12.2%
b 3333
 
6.4%
l 3333
 
6.4%
u 3333
 
6.4%
w 3300
 
6.3%
h 3300
 
6.3%
i 3300
 
6.3%
t 3300
 
6.3%
d 3240
 
6.2%
Other values (2) 6248
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12997
25.0%
r 6364
12.2%
b 3333
 
6.4%
l 3333
 
6.4%
u 3333
 
6.4%
w 3300
 
6.3%
h 3300
 
6.3%
i 3300
 
6.3%
t 3300
 
6.3%
d 3240
 
6.2%
Other values (2) 6248
12.0%

20
Real number (ℝ)

Distinct137
Distinct (%)1.1%
Missing9
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean31.522144
Minimum24.3
Maximum38.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.7 KiB
2023-05-10T07:02:34.587151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile26.6
Q129.1
median31.7
Q334.2
95-th percentile35.7
Maximum38.5
Range14.2
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation2.9605914
Coefficient of variation (CV)0.093921005
Kurtosis-1.086417
Mean31.522144
Median Absolute Deviation (MAD)2.5
Skewness-0.20895987
Sum409409.6
Variance8.7651014
MonotonicityNot monotonic
2023-05-10T07:02:34.863685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1 226
 
1.7%
34.9 196
 
1.5%
35.4 195
 
1.5%
35.2 194
 
1.5%
35 182
 
1.4%
35.3 179
 
1.4%
34.8 178
 
1.4%
35.5 171
 
1.3%
33.5 164
 
1.3%
35.6 163
 
1.3%
Other values (127) 11140
85.7%
ValueCountFrequency (%)
24.3 2
 
< 0.1%
24.5 1
 
< 0.1%
24.6 3
 
< 0.1%
24.7 4
 
< 0.1%
24.8 6
 
< 0.1%
24.9 13
0.1%
25 3
 
< 0.1%
25.1 12
0.1%
25.2 21
0.2%
25.3 18
0.1%
ValueCountFrequency (%)
38.5 4
< 0.1%
38.4 2
< 0.1%
38.3 2
< 0.1%
38 2
< 0.1%
37.9 1
 
< 0.1%
37.7 2
< 0.1%
37.6 1
 
< 0.1%
37.4 1
 
< 0.1%
37.2 2
< 0.1%
37.1 1
 
< 0.1%

Interactions

2023-05-10T07:02:19.475030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:48.970266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:51.876380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:54.887836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:58.799054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:01.441214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:04.098457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:07.004757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:10.276064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:13.756642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:16.483136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:19.727566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:49.388153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:52.136334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:55.236014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:59.046098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:01.688059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:04.349276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:07.237303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:10.611247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:14.025675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:16.726889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:19.946878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:49.617695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:52.347948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:55.578334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:59.257986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:01.917213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:04.587864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:07.448243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:10.982018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:14.261610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:16.974524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:20.190268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:49.870431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:52.567327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:55.950323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:59.509721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:02.156961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:04.826758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:07.690433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:11.348382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:14.494797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:17.216220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:20.423787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:50.136725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:52.773794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:56.295848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:59.734352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:02.380603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:05.058110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:07.909892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:11.672917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:14.755661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:17.448990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:20.672924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:50.371302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:53.020951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:56.682900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:59.973102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:02.637792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:05.300259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:08.167385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:12.075910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:15.022701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:17.698468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:20.924906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:50.620685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:53.264878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:57.092325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:00.235151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:02.880766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:05.555119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:08.514657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:12.484472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:15.272918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:17.967534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:21.163990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:50.866908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:53.572326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:57.684299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:00.466711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:03.102643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:05.790330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:08.849755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:12.796947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:15.503285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:18.474933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:21.390820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:51.113982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:53.879699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:58.009675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:00.678434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:03.335062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:06.005130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:09.200096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:13.019149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:15.733324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:18.705699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:21.648123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:51.371122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:54.237360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:58.294087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:00.927836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:03.597964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:06.484246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:09.588264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:13.265992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:15.994516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:18.975462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:21.916592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:51.632967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:54.556060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:01:58.557297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:01.174641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:03.842244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:06.748914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:09.944770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:13.507774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:16.240675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-10T07:02:19.229256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-10T07:02:35.133337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2346910111215162015781314171819
21.000-0.277-0.3280.1090.0030.0150.4680.4510.3990.372-0.0590.0550.0240.0360.0190.0730.0560.2200.1450.013
3-0.2771.0000.3680.2490.2000.239-0.502-0.281-0.167-0.2270.5820.2340.1040.1190.0970.1470.1490.2160.1300.008
4-0.3280.3681.0000.0100.0300.105-0.446-0.475-0.545-0.5800.2140.1840.0910.0970.0860.0840.0750.3780.4750.011
60.1090.2490.0101.0000.6720.717-0.143-0.0790.1120.1210.1720.3340.1280.1750.1270.1360.1170.1520.2310.017
90.0030.2000.0300.6721.0000.574-0.227-0.2000.0570.0760.1190.3690.1360.1750.1280.0730.0600.0450.0340.026
100.0150.2390.1050.7170.5741.000-0.090-0.0650.0530.0190.2600.3810.1410.1470.1630.1010.0910.0150.0680.026
110.468-0.502-0.446-0.143-0.227-0.0901.0000.6330.4550.403-0.0760.1720.0830.1040.0900.0820.0850.4620.3450.000
120.451-0.281-0.475-0.079-0.200-0.0650.6331.0000.5320.5670.0150.1290.0960.1180.0910.1150.1010.4190.5690.015
150.399-0.167-0.5450.1120.0570.0530.4550.5321.0000.6810.0120.1680.0790.0830.0730.0990.0820.3650.3830.000
160.372-0.227-0.5800.1210.0760.0190.4030.5670.6811.000-0.0260.1690.0840.0930.0840.1070.0910.3340.4670.017
20-0.0590.5820.2140.1720.1190.260-0.0760.0150.012-0.0261.0000.4930.1710.1540.1800.1940.2180.1820.1260.011
10.0550.2340.1840.3340.3690.3810.1720.1290.1680.1690.4931.0000.4440.3750.4720.2130.2520.0610.0580.010
50.0240.1040.0910.1280.1360.1410.0830.0960.0790.0840.1710.4441.0000.2190.3510.0750.0780.1720.1710.000
70.0360.1190.0970.1750.1750.1470.1040.1180.0830.0930.1540.3750.2191.0000.1860.0830.0730.1880.2020.025
80.0190.0970.0860.1270.1280.1630.0900.0910.0730.0840.1800.4720.3510.1861.0000.0940.1050.1590.1950.018
130.0730.1470.0840.1360.0730.1010.0820.1150.0990.1070.1940.2130.0750.0830.0941.0000.2120.2700.3200.018
140.0560.1490.0750.1170.0600.0910.0850.1010.0820.0910.2180.2520.0780.0730.1050.2121.0000.2230.2860.011
170.2200.2160.3780.1520.0450.0150.4620.4190.3650.3340.1820.0610.1720.1880.1590.2700.2231.0000.3450.000
180.1450.1300.4750.2310.0340.0680.3450.5690.3830.4670.1260.0580.1710.2020.1950.3200.2860.3451.0000.013
190.0130.0080.0110.0170.0260.0260.0000.0150.0000.0170.0110.0100.0000.0250.0180.0180.0110.0000.0131.000

Missing values

2023-05-10T07:02:22.352266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T07:02:23.096848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-10T07:02:23.930165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

01234567891011121314151617181920
02009-03-08Changi0.07.011.2E35.0EESE15.019.060.054.0MEDMed4.05.0NoNoblue33.4
12014-11-12Woodlands0.08.49.6WNW37.0NNENW13.028.060.056.0MeDLOw1.02.0NoneYesblue35.4
22008-08-08Woodlands0.07.611.1ESE52.0SEE19.019.0NaN13.0MedMEd0.00.0NoneNoblue32.8
32015-10-12Changi0.07.211.4NE31.0NNWNE9.013.051.053.0hIghmed1.01.0NoNored31.4
42013-10-27Woodlands5.67.08.5NNW33.0NN13.019.068.064.0meDMeD6.05.0NoneNogrey35.4
52014-02-08Tuas0.010.612.8S43.0SSESSW15.022.054.045.0MEdMED1.00.0NoNogrey31.9
62009-01-23Woodlands81.07.84.7SSW28.0SN13.015.089.058.0LOWlOW8.07.0YesNored34.4
72016-02-02Tuas0.07.212.7SSW41.0SSESSW13.017.049.045.0meDmeD0.00.0NoNowhite29.9
82016-05-07Sentosa0.05.25.8N43.0NN17.015.054.039.0MEDmEd2.07.0NoYesblue30.0
92013-06-28Changi1.80.82.1ESE22.0WSWESE7.09.084.068.0mEDmEd7.06.0YesYesblue30.0
01234567891011121314151617181920
129872012-07-30Changi0.04.29.9WSW17.0SWW6.06.066.032.0mEdmed1.01.0NoNowhite28.1
129882009-01-03Changi4.45.40.0SSE41.0SESSE15.015.085.086.0MedmeD8.08.0YesYesblue32.7
129892010-02-21Sentosa0.014.810.4N57.0SSWSSE13.020.067.046.0MeDmed6.04.0NoneNoblue33.3
129902014-09-26Tuas0.04.010.9NNW39.0NNEWNW13.017.065.047.0MeDmed3.02.0NoneYesblue29.4
129912011-08-21Changi3.04.41.9SE37.0SSWSE6.017.089.051.0HIghHigH8.07.0YesYesred29.2
129922015-06-12Tuas0.41.65.6SW28.0NoneSW0.013.092.066.0MEdmED4.03.0NoNored29.3
129932016-09-29Changi0.07.40.6NNW35.0NNNW4.09.057.048.0LOwLoW7.08.0NoYeswhite31.3
129942011-05-14Sentosa8.00.85.4S52.0WSWS24.028.087.064.0HiGHHIgHNaNNaNYesYesgrey26.4
129952011-02-14Sentosa4.44.412.0S31.0SSESSE13.09.068.052.0hiGHhIgh5.01.0YesNoblue30.5
129962016-03-04Changi0.48.411.1E33.0ESEE4.011.066.044.0mEDMeD4.02.0NoNoblue33.4

Duplicate rows

Most frequently occurring

01234567891011121314151617181920# duplicates
9042015-08-22Woodlands0.06.09.9N33.0ENNW15.024.077.054.0MeDmeD3.01.0NoNoblue33.54
282008-09-15Changi1.06.29.7NE28.0SNNE2.015.072.061.0meDmed1.01.0NoNoblue30.93
372008-10-06Tuas0.05.211.1SE30.0ENESE13.013.057.034.0higHhiGh2.05.0NoNored27.73
462008-11-01Changi0.08.2-9.8ESE41.0SSWESE7.020.048.050.0mEdmed3.05.0NoNoblue33.13
752009-01-16Changi0.09.012.4NE31.0WNWNE7.015.054.052.0mEDMed1.06.0NaNNored33.93
762009-01-19Sentosa0.07.813.4N54.0NWNW28.022.052.020.0mEDMEd1.01.0NoNogrey33.13
952009-03-18Tuas0.84.09.8SSW33.0SWSW7.015.084.039.0mEDMEd6.01.0NoNoblue30.73
982009-03-25Sentosa2.42.23.2SSE31.0NS6.019.095.056.0medMED7.07.0YesNowhite30.73
992009-03-27Tuas0.06.810.7SW31.0NaNWSW0.017.069.025.0MeDMED0.00.0NoNoblue33.53
1072009-04-13Tuas0.012.20.1NE41.0ESSE15.06.051.074.0meDmeD7.08.0NaNYesgrey32.23